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Sinem Aslan
Intel
32Publications
7H-index
119Citations
Publications 32
Newest
May 2, 2019 in CHI (Human Factors in Computing Systems)
#1Sinem Aslan (Intel)H-Index: 7
#2Nese Alyuz (Intel)H-Index: 11
Last.Asli Arslan Esme (Intel)H-Index: 5
view all 7 authors...
We developed a real-time, multimodal Student Engagement Analytics Technology so that teachers can provide just-in-time personalized support to students who risk disengagement. To investigate the impact of the technology, we ran an exploratory semester-long study with a teacher in two classrooms. We used a multi-method approach consisting of a quasi-experimental design to evaluate the impact of the technology and a case study design to understand the environmental and social factors surrounding t...
#1Nese Alyuz (Intel)H-Index: 11
#2Eda Okur (Intel)H-Index: 4
Last.Asli Arslan Esme (Intel)H-Index: 5
view all 6 authors...
We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse. Final behavioral engagement states are achieved by fusing modality-specific classifiers at the decision level. Various experiments were conducted on a student dataset collected in an authentic classroom.
#1Eda OkurH-Index: 4
#2Sinem AslanH-Index: 7
Last.Ryan S. BakerH-Index: 43
view all 5 authors...
The development of real-time affect detection models often depends upon obtaining annotated data for supervised learning by employing human experts to label the student data. One open question in annotating affective data for affect detection is whether the labelers (i.e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost feasibility of obtaining the labels. In this study, we investigate the following research questions: For affective stat...
#1Eda Okur (Intel)H-Index: 4
#2Nese Alyuz (Intel)H-Index: 11
Last.Asli Arslan Esme (Intel)H-Index: 5
view all 6 authors...
To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavioral engagement. Incorporating the contextual information improved the overall F1-scores from 0.77 to 0.82. Our cross-classroom and cross-platform experiments showed the proposed g...
#1Sinem Aslan (Intel)H-Index: 7
#2Nese Alyuz (Intel)H-Index: 11
Last.Asli Arslan Esme (Intel)H-Index: 5
view all 6 authors...
The goal of this research is to investigate the effect of emotion-aware interventions on students’ behavioral and emotional states. To this end, we collected data from 12 students in the 9th grade in a high school in Turkey. The data collection took place in two sessions of an English Course. While the students were reading articles and solving relevant questions, our data collection application running in the background recorded the videos of the individual students through a camera and capture...
#1Sinem Aslan (Intel)H-Index: 7
#2Eda Okur (Intel)H-Index: 4
Last.Ryan S. Baker (UPenn: University of Pennsylvania)H-Index: 43
view all 5 authors...
There is still considerable disagreement on key aspects of affective computing - including even how affect itself is conceptualized. Using a multi-modal student dataset collected while students were watching instructional videos and answering questions on a learning platform, we investigated the two key paradigms of how affect is represented through a comparative approach: (1) Affect as a set of discrete states and (2) Affect as a combination of a two-dimensional space of attributes. We specific...
#1Eda Okur (Intel)H-Index: 4
#2Sinem Aslan (Intel)H-Index: 7
Last.Ryan S. Baker (UPenn: University of Pennsylvania)H-Index: 43
view all 5 authors...
The development of real-time affect detection models often depends upon obtaining annotated data for supervised learning by employing human experts to label the student data. One open question in labeling affective data for affect detection is whether the labelers (i.e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost and feasibility of obtaining the labels. In this study, we investigate the following research questions: For affective st...
#1Sinem Aslan (Intel)H-Index: 7
#2Eda Okur (Intel)H-Index: 4
Last.Ryan S. Baker (UPenn: University of Pennsylvania)H-Index: 43
view all 5 authors...
Affect has emerged as an important part of the interaction between learners and computers, with important implications for learning outcomes. As a result, it has emerged as an important area of research within learning analytics. Reliable and valid data labeling is a key tenet for training machine learning models providing such analytics. In this study, using Human Expert Labeling Process (HELP) as a baseline labeling protocol, we investigated an optimized method through several experiments for ...
#1Nese Alyuz (Intel)H-Index: 11
#2Sinem Aslan (Intel)H-Index: 7
Last.Asli Arslan Esme (Intel)H-Index: 5
view all 5 authors...
Automated driving has the potential to reduce the amount of fatal crashes, lighten the burden of commutes, and democratize mobility access to wider populations. But delegation of control to automation is not without issues. One of the foreseen drawbacks is that users might experience negative emotional reactions to unanticipated or unexplainable automated maneuvers. In this paper we present a novel method to induce targeted emotional reactions, frustration and startle, in simulated automated dri...
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